Frédéric Nowak

Distributed tracing with OpenTelemetry changed our approach to debugging Go microservices. Before: grep through logs of 27 services, manual timeline reconstruction, guessing games. After: one trace ID → visualization of entire request flow → instant problem identification. Payment Service slowing down to 2.8s? Visible immediately. N+1 query after deployment? Found in 15 minutes. Connection leak? Discovered faster than water boils. Full implementation guide with Go examples: https://lnkd.in/dnYdFKqg #golang #microservices #observability #opentelemetry #tempo #jaeger #distributedtracing #sre

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This article says "Amazon Web Services (AWS) unveils Ocelot and it's bad news for Alice & Bob". I beg to disagree: I think it's great news! We (= AWS, Alice & Bob and virtually everyone else) are still a few years away from commercially useful machines. At this point, any achievement by any player increases the confidence that we will actually see commercially useful machines. So, seeing Ocelot run a first error correction experiment is great news for everyone. And that Ocelot uses cat qubits similar to those developed by Alice & Bob is even greater news, because it shows that cat qubits aren't just a nice theoretical idea. It shows cat qubits can be used to implement quantum correction on a real device, even in a suboptimal setup featuring transmons. In other words: a rising tide lifts all boats. Of course, there will come a point when the pioneer spirit of today will yield to the commercial battle for juicy contracts with corporate end users. But we're not there yet. So, let's cheer the achievements of others while we still can! Great job AWS! I also recommend reading the whole article (with a translator if needed), it's more nuanced than the title suggests.

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🚀 Just released a personal project: kpipe – a modern, functional, and high-performance Kafka consumer built with Java 23. kpipe is designed to make it easier (and faster) to build Kafka pipelines using modern Java features and a clean, composable API. 🔹 Highlights: ⚡ Virtual threads for lightweight, scalable concurrency 🧩 Composable message processors for flexible pipeline logic 🚀 DslJson for fast, zero-dependency JSON serialization 📊 Built-in metrics, robust error handling, and configurable retries 🔀 Support for both parallel and sequential processing 📦 Almost zero dependencies — minimal, fast, and easy to integrate Ideal for large-scale systems where performance, simplicity, and developer experience matter. 📂 GitHub: https://lnkd.in/gzrSXqQV 💬 Feedback, ideas, and contributions welcome! #Kafka #Java #VirtualThreads #HighPerformance #OpenSource #Streaming

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New Form3 .tech podcast has just dropped! In this episode I caught up with Jean-Baptiste Leroy from GCP to talk about running containers on GCP! When you come to run a container on the cloud you are presented with a wealth of different options, what is the difference between them? Which one should you choose and for which project? Listen to this episode as JB guides us through the different options for running containers on GCP and which option is right for your use case. Link in the comments as always, enjoy!

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Architecture is what turns a platform from “something that works” into “something that scales.” As platform engineering grows, the role of the architect becomes more important than ever. It’s not just about Kubernetes or automation, but it’s about designing platforms that are reliable, compliant, and secure by default. The kind of foundation that allows developers to move faster without breaking everything. That’s exactly why we built the Platform Engineering Certified Architect course. It’s designed for architects, platform engineers, and security engineers who want to go beyond theory and understand what it really takes to build scalable, secure Internal Developer Platforms. Throughout the course, we go deep into Policy as Code, Zero Trust, compliance and security patterns, and platform automation with Kubernetes Operators. You’ll also learn how to design developer-facing APIs and CLIs, and bring everything together in a real hands-on capstone project. If you’re building the next generation of platform architecture or preparing to lead the team that does then this is where you start. https://lnkd.in/gBA56gw6 #policyascode #zerotrust #compliance #security #platformautomation #platformcli #platformapi #platformarchitecture #platformetrics #platformengineering #devops #sre #kubernetes

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Kafka Stability in Production — Lessons from the SRE Frontline Running Kafka in production isn’t just about keeping clusters alive — it’s about keeping the flow of business data continuous. As SREs, we often learn the hard way that “Kafka is stable” only when your producers, consumers, and brokers all agree on time, retries, and retention. A single misconfigured replication factor or ISR shrinkage during broker maintenance can bring unpredictable lag spikes. We tackled this by focusing on observability-first reliability — tracking lag trends, controller elections, and GC pauses before incidents occurred. The result? Mean time to detect dropped by ~40%, and Kafka rebalance storms reduced dramatically. SRE isn’t about chasing incidents; it’s about building intuition for failure patterns. Kafka taught us this — stability is never an event, it’s an ongoing culture of operational empathy.

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Iceberg won the table format wars, but after victory comes the unseen pain of real-time colliding with Iceberg's architecture:  • Snapshot overload and painful expiration workflows 🕰️  • Small-file hell and the constant battle for compaction 🔥  • Why Partition evolution is not as simple as it first appears 🧨 We also present a fundamentally different approach that keeps data in Kafka and projects it logically as Iceberg. No file movement. No snapshot churn. No endless compaction jobs. 👉 Read the full post here: https://lnkd.in/eq3vgnzJ #apacheiceberg #kafka #dataengineering #lakehouse #streamingdata #streambased #moderninfrastructure

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Announcing Chimp: build type-safe, boilerplate-less #MCP servers in #Scala using the HTTP transport! Below is a self-contained, scala-cli runnable example ⤵️ Fun fact: most of Chimp's code is auto-generated using Cursor with the help of Scala Metals MCP server. Tasks such as generating the MCP JSON-RCP model - basing on the Typescript schema, writing tests or "filling in" missing code given a non-compiling API sketch turn out to be a great fit of LLMs. The tight feedback loop provided by the compiler helps a lot - Cursor nicely iterates on the generated code, fixing any problems. You do need to have quite a good idea how you'd like the code to look, to guide the generation, though. There are still limits to the magic ;).

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